System and method for facilitating efficient damage assessments

ABSTRACT

Embodiments described herein provide a system for facilitating image sampling for training a target detector. During operation, the system obtains a first image depicting a first target. Here, the continuous part of the first target in the first image is labeled and enclosed in a target bounding box. The system then generates a set of positive image samples from an area of the first image enclosed by the target bounding box. A respective positive image sample includes at least a part of the first target. The system can train the target detector with the set of positive image samples to detect a second target from a second image. The target detector can be an artificial intelligence (AI) model capable of detecting an object.

RELATED APPLICATION

Under 35 U.S.C. 119, this application claims the benefit and right ofpriority of Chinese Patent Application No. 201811046061.6, filed 7 Sep.2018.

BACKGROUND Field

This disclosure is generally related to the field of artificialintelligence. More specifically, this disclosure is related to a systemand method for facilitating efficient indexing in a database system.

Related Art

In conventional a damage assessment technique, a vehicle insurancecompany may send a professional claim adjuster to the location of adamaged vehicle to conduct a manual survey and damage assessment. Thesurvey and damage assessment conducted by the adjuster can includedetermining a repair solution, estimating an indemnity, takingphotographs of the vehicle, and archiving the photographs for subsequentassessment of the damage by a damage inspector at the vehicle insurancecompany. Since the survey and subsequent damage assessment are performedmanually, an insurance claim may require a significant number of days toresolve. Such delays in the processing time can lead to poor userexperience with the vehicle insurance company. Furthermore, the manualassessments may also incur a large cost (e.g., labor, training,licensing, etc.).

To address this issue, some vehicle insurance companies use image-basedartificial intelligence (AI) models (e.g., machine-learning-basedtechniques) for assessing vehicle damages. Since the AI models mayautomatically detect the damages on a vehicle based on images, theautomated assessment technique can shorten the wait time and reducelabor costs. For example, an AI-based assessment technique can be usedfor automatic identification of the damage of the vehicle (e.g., theparts of the vehicle). Typically, a user can capture a set of images ofthe vehicle depicting the damages from the user's location, such as theuser's home or work, and send the images to the insurance company (e.g.,using an app or a web interface). These images can be used by an AImodel to identify the damage on the vehicle. In this way, the automatedassessment process may reduce the labor costs for a vehicle insurancecompany and improve user experience associated the claim processing.

Even though automation has brought many desirable features to a damageassessment system, many problems remain unsolved in universal damagedetection (e.g., independent of the damaged parts).

SUMMARY

Embodiments described herein provide a system for facilitating imagesampling for training a target detector. During operation, the systemobtains a first image depicting a first target. Here, the continuouspart of the first target in the first image is labeled and enclosed in atarget bounding box. The system then generates a set of positive imagesamples from an area of the first image enclosed by the target boundingbox. A respective positive image sample includes at least a part of thefirst target. The system can train the target detector with the set ofpositive image samples to detect a second target from a second image.The target detector can be an artificial intelligence (AI) model capableof detecting an object.

In a variation on this embodiment, the first and second targets indicatea first and second vehicular damages, respectively. The label of thecontinuous part indicate a material impacted by the first vehiculardamage.

In a further variation, the system detects the second target bydetecting the second vehicular damage based on a corresponding materialindependent of identifying a part of a vehicle impacted by the secondvehicular damage.

In a variation on this embodiment, the system generates the set ofpositive image samples by determining a region proposal in the area ofthe first image enclosed by the target bounding box and selecting theregion proposal as a positive sample if an overlapping parameter of theregion proposal is in a threshold range.

In a further variation, the overlapping parameter is a ratio of anoverlapping region and a surrounding region of the region proposal. Theoverlapping region indicates a common region covered by both the regionproposal and a set of internal bounding boxes within the target boundingbox. A respective internal bounding box can include at least a part ofthe continuous region. The surrounding region indicates a total regioncovered by the region proposal and the set of internal bounding boxes

In a further variation, the system selects the set of internal boundingboxes based on one of an intersection with the region proposal, adistance from the region proposal, and a total number of internalbounding boxes in the target bounding box.

In a further variation, the system generates a negative sample, whichexcludes any part of the first target, from the first image. To do so,the system can select the region proposal as the negative sample inresponse to determining that the overlapping parameter of the regionproposal is in a low threshold range. The system may also select an areaoutside of the target bounding box in the first image as the negativesample

In a further variation, the system determines a set of subsequent regionproposals in the area of the first image enclosed by the target boundingbox. To do so, the system can apply a movement rule to a previous regionproposal and terminate based on a termination condition.

In a variation on this embodiment, the system generates a second set ofpositive image samples. To do so, the system can select a positive imagesample from a region proposal in a second target bounding box in thefirst image. The system may also change the size or shape of a boundingbox of a region proposal of a previous round.

In a variation on this embodiment, the system optimizes the training ofthe target detector by generating a plurality of bounding boxes for aplurality of image samples in the set of positive image samples andcombining the plurality of bounding boxes to generate a combinedbounding box and a corresponding label. Here, a respective bounding boxidentifies the corresponding part of the continuous region.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A illustrates exemplary infrastructure and environmentfacilitating an efficient assessment system, in accordance with anembodiment of the present application.

FIG. 1B illustrates exemplary training and operation of an efficientassessment system, in accordance with an embodiment of the presentapplication.

FIG. 2 illustrates exemplary bounding boxes for generating image samplesfor training a target detection system of an efficient assessmentsystem, in accordance with an embodiment of the present application.

FIG. 3A illustrates an exemplary region proposal generation process forgenerating image samples, in accordance with an embodiment of thepresent application.

FIG. 3B illustrates an exemplary assessment of a region proposal forgenerating image samples, in accordance with an embodiment of thepresent application.

FIG. 3C illustrates an exemplary determination of whether a regionproposal can be an image sample, in accordance with an embodiment of thepresent application.

FIG. 4 illustrates an exemplary integration of detection results ofmultiple samples, in accordance with an embodiment of the presentapplication.

FIG. 5A presents a flowchart illustrating a method of an assessmentsystem performing a damage assessment, in accordance with an embodimentof the present application.

FIG. 5B presents a flowchart illustrating a method of an assessmentsystem generating image samples for training a target detection system,in accordance with an embodiment of the present application.

FIG. 5C presents a flowchart illustrating a method of an assessmentsystem integrating detection results of multiple samples, in accordancewith an embodiment of the present application.

FIG. 6 illustrates an exemplary computer system that facilitates anefficient assessment system, in accordance with an embodiment of thepresent application.

FIG. 7 illustrates an exemplary apparatus that facilitates an efficientassessment system, in accordance with an embodiment of the presentapplication.

In the figures, like reference numerals refer to the same figureelements.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled inthe art to make and use the embodiments, and is provided in the contextof a particular application and its requirements. Various modificationsto the disclosed embodiments will be readily apparent to those skilledin the art, and the general principles defined herein may be applied toother embodiments and applications without departing from the spirit andscope of the present disclosure. Thus, the embodiments described hereinare not limited to the embodiments shown, but are to be accorded thewidest scope consistent with the principles and features disclosedherein.

Overview

The embodiments described herein solve the problem of efficientlydetecting a damage of a vehicle by (i) generating positive and negativeimage samples from a labeled image for training a target detectionsystem; and (ii) integrating detection results of multiple image samplesassociated with a damage to increase the efficiency of the targetdetection system. In this way, an assessment system can use the targetdetection system to identify damages on a vehicle independent of thedamaged parts and generate a repair plan based on the identification.

With existing technologies, an AI-based technique for determiningvehicular damages from an image may include determining the damagedparts of a vehicle and the degree of the damages based on the similarimages in historical image data. Another technique may involveidentifying the area of a damaged part in the center of an input imagethrough an identification method and comparing the area of the part withthe historical image data to obtain a similar image. By comparing theobtained image with the input image, the technique may determine thedegree of damages. However, these techniques are prone to interferencefrom the additional information of the damaged part in the input image,a reflection of light, contaminants, etc. As a result, these techniquesmay operate with low accuracy while determining the degree of damages.

For example, for identifying damages on a vehicle using targetdetection, the technique typically needs to be trained with a certainnumber of positive samples and negative samples. Here, a certain numberof images depicting the damages need to serve as the positive samples,and a certain number of images not depicting the damages need to serveas the negative samples. However, obtaining positive samples insufficient numbers can be challenging. Furthermore, a negative samplemay include at least a segment of a damaged part and cause interferencein the training process. As a result, the AI-based model may not beequipped to detect damages on a part of a vehicle, especially if themodel has not been trained with similar damages on the part of thevehicle.

To solve this problem, embodiments described herein provide anassessment system that can identify a damaged area of a vehicle (i.e., atarget) from one or more images of the vehicle and assess the degree ofdamage on the identified damaged area. The system can assess damage to avehicle in two dimensions. The system can identify a part of the vehiclebased on object detection in one dimension and determine the damage inanother dimension. To determine the damage, the system can identify thedamaged area based on the material on which the damage has beeninflicted. Hence, the system can execute the damage detectionindependent of the underlying vehicle part. This allows the system toefficiently detect a damaged area on a vehicle without relying on howthat damaged area may appear on a specific part of the vehicle.

To do so, the system can identify damages and the degree of damages onmaterials, such as the paint surface, plastic components, frostedcomponents, glasses, lights, mirrors, etc., without requiringinformation of the underlying parts. As a result, the system can also beused for the identification of damages on similar materials in otherscenarios (i.e., other than the damages on a vehicle). On the otherhand, the system can independently identify one or more parts that mayrepresent the damaged area. In this way, the system can identify thedamaged area and the degree of damages, and the parts that construct thedamaged area. Based on the damage information, the system then performsa damage assessment, determines a repair plan, and generates a costestimate. For example, the system can estimate the cost and/oravailability of the parts, determine whether a repair or replacement isneeded based the degree of damage, determine the deductibles and fees,and schedule a repair operation based on calendar information of arepair shop.

However, the target detector (e.g., a deep-learning network) can operatewith high accuracy if the target detector is trained with sufficientnumber of positive and negative samples. In some embodiments, the systemcan also generate image samples from labeled images (e.g., images withlabeled targets). A labeled image may at least include a target boundingbox that can be hand-labeled in advance and a plurality of internalbounding boxes in the target bounding box. The target bounding box isused for surrounding a continuous region of a target (e.g., the largestcontinuous region of damage), and each of the plurality of internalbounding boxes surrounds a segment of the continuous region of thetarget.

During operation, the system can obtain the labeled images and determineregion proposals for sampling in the target bounding box. The regionproposal can be represented based on a pre-determined bounding box(e.g., with predetermined size and shape). This bounding box can beplaced in the target bounding box based on a sliding window or an imagesegmentation algorithm. The system then compares the region proposalwith the corresponding internal bounding boxes to determine overlappingparameters.

Based on whether the overlapping parameters are in a threshold range,the system may collect the region proposal as a positive sample fortraining the target detector. Otherwise, if the overlapping parametersare below a low threshold range, the system may collect the regionproposal as a negative sample. In addition, the system can also collectnegative samples from outside of the target bounding box to ensure thatthe negative sample does not include any damage information. In thisway, the system can reduce interference and improve the accuracy of thetarget detector.

Exemplary System

FIG. 1A illustrates exemplary infrastructure and environmentfacilitating an efficient assessment system, in accordance with anembodiment of the present application. In this example, aninfrastructure 100 can include an automated assessment environment 110.Environment 110 can facilitate automated damage assessment in adistributed environment. Environment 110 can serve a client device 102using an assessment server 130. Server 130 can communicate with clientdevice 102 via a network 120 (e.g., a local or a wide area network, suchas the Internet). Server 130 can include components such as a number ofcentral processing unit (CPU) cores, a system memory (e.g., a dualin-line memory module), a network interface card (NIC), and a number ofstorage devices/disks. Server 130 can run a database system (e.g., adatabase management system (DBMS)) for maintaining database instances.

Suppose that a user 104 needs to file an insurance claim regardingdamage 124 on a vehicle. If the insurance company deploys an AI-basedtechnique for automatically determining damages from an image, user 104may use client device 102 to capture an image 122 depicting damage 124.User 104 can then send an insurance claim 132 comprising image 122 as aninput image from client device 102 via network 120. With existingtechnologies, the AI-based technique may determine the parts damaged bydamage 124 and the degree of damage 124 based on the similar images inhistorical image data. Another technique may involve identifying thearea of damage 124 in the center of input image 122 through anidentification method and comparing the area of damage 124 with thehistorical image data to obtain a similar image. By comparing theobtained image with input image 122, the technique may determine thedegree of damages.

However, these techniques are prone to interference from the additionalinformation in input image 122, such as undamaged segments, a reflectionof light, contaminants, etc. As a result, these techniques may operatewith low accuracy while determining the degree of damages. Furthermore,these techniques typically need to be trained with a certain number ofpositive samples and negative samples. However, obtaining positivesamples in sufficient numbers can be challenging. Furthermore, anegative sample may include interfering elements. As a result, theAI-based technique may not be equipped to detect damage 124, especiallyif the technique has not been trained with damages similar to damage124.

To solve these problems, an automated assessment system 150 canefficiently and accurately identify the area and vehicle parts impactedby damage 124 (i.e., one or more targets) from image 122, and assess thedegree of damage 124. System 150 can run on server 130 and communicatewith client device 102 via network 120. In some embodiments, system 150includes a target detector 160 that can assess damage 124 in twodimensions. Target detector 160 can identify a part of the vehicleimpacted by damage 124 in one dimension and determine damage 124 inanother dimension. Upon determining the damage, target detector 160 canapply geometric calculation and division to determine the degree ofdamage 124 as target 126 that can include the location of damage 124,the parts impacted by damage 124, and the degree of damage 124.

Furthermore, target detector 160 can identify the area or location ofdamage 124 based on the material on which the damage has been inflicted.As a result, target detector 160 can execute the damage detectionindependent of the underlying vehicle part. This allows target detector160 to efficiently detect the area or location of damage 124 withoutrelying on how that damaged area may appear on a specific part of thevehicle. In other words, target detector 160 can identify damage 124 andthe degree of damage 124 on the material on which damage 124 appearswithout requiring information of the underlying parts. In addition,target detector 160 can independently identify one or more parts thatmay be impacted by damage 124. In this way, target detector 160 canidentify the area and the degree of damage 124, and the parts impactedby damage 124.

Based on the damage information generated by target detector 160, system150 then generates a damage assessment 134 to determine a repair planand generate a cost estimate for user 102. System 150 can estimate thecost and/or availability of the parts impacted by damage 124, determinewhether a repair or replacement is needed based the degree of damage124, and schedule a repair operation based on calendar information of arepair shop. System 150 can then send assessment 134 to client device102 via network 120.

Examples of target detector 160 include, but are not limited to, FasterRegion-Convolutional Neural Network (R-CNN), You Only Look Once (YOLO),Single Shot MultiBox Detector (SSD), R-CNN, Lighthead R-CNN, andRetinaNet. In some embodiments, target detector 160 can reside on clientdevice 102. Target detector 160 can then use a mobile end targetdetection technique, such as MobileNet+SSD.

FIG. 1B illustrates exemplary training and operation of an efficientassessment system, in accordance with an embodiment of the presentapplication. Target detector 160 can operate with high accuracy iftarget detector 160 is trained with sufficient number of positive andnegative samples. In some embodiments, system 150 can also generateimage samples 170 from a labeled image 172, which can be an image withlabeled targets. It should be noted that the image sampling and targetdetection can be executed on the same or different devices. Labeledimage 172 may at least include a target bounding box 180 that can behand-labeled in advance and a plurality of internal bounding boxes 182and 184 in target bounding box 180. Target bounding box 180 is used forsurrounding a continuous region of a target (e.g., the largestcontinuous region of damage), and each of internal bounding boxes 182and 184 surrounds a segment of the continuous region of the target.

The labeling of on image 172 can indicate a damage definition, which caninclude the area and the class, associated with a respective continuousdamage segment depicted in image 172. The various degrees of damagescorresponding to the various materials are defined as the damageclasses. For example, if the material is glass, the damage class caninclude minor scratches, major scratches, glass cracks, etc. Thesmallest area that includes a continuous segment of the damage isdefined as the area of the damage segment. Therefore, for eachcontinuous segment of the damage in image 172, the labeling can indicatethe area and the class of the damage segment. Upon determining thedamage definition of a respective damage segment, the labeling canindicate the damaged definition of the corresponding damage segment.With such labeling of a damage segment, the damage becomes related onlyto the material and not to a specific part.

During operation, system 150 can obtain labeled image 172 and determineregion proposals for sampling in target bounding box 180. The regionproposal can be represented based on a pre-determined bounding box(e.g., with predetermined size and shape). The bounding box of theregion proposal can be placed in target bounding box 180 based on asliding window or an image segmentation algorithm. System 150 thencompares the region proposal with the corresponding internal boundingboxes to determine overlapping parameters (e.g., an intersection overunion (IoU)). System 150 may only compare the region proposal with theinternal bounding boxes that are within a distance threshold of theregion proposal (e.g., 50 pixels) or have an intersection with theregion proposal.

Based on whether the overlapping parameters are in a threshold range(e.g., greater than 0.7 or falls within 0.7-0.99), system 150 maycollect the region proposal as a positive sample for training targetdetector 160. Otherwise, if the overlapping parameters are below a lowthreshold range (e.g., less than 0.1), system 150 may collect the regionproposal as a negative sample. In addition, system 150 can also collectnegative samples from outside of target bounding box 180 to ensure thatthe negative sample does not include any damage information. In thisway, system 150 can generate image samples 170 that can include accuratepositive and negative samples. By training object detector 160 usingimage samples 170, system 150 can reduce the interference and improvethe accuracy of target detector 160, thereby allowing target detector160 to accurately detect target 124 from input image 122.

Image Sampling

FIG. 2 illustrates exemplary bounding boxes for generating image samplesfor training a target detection system of an efficient assessmentsystem, in accordance with an embodiment of the present application.During operation, system 150 can receive an input image 200 for imagesampling. Image 200 may depict a vehicular damage 220 (i.e., damage on avehicle). A user 250 may label image 200 with one or more boundingboxes. Each of the bounding boxes can correspond to a label thatindicates a damage definition (i.e., the area of the damage and theclass of damage). The bounding boxes include at least one targetbounding box and may include a set of internal bounding boxes located inthe target bounding box.

User 250 may determine the largest continuous region of damage 220 andapply a target bounding box 202 on the largest continuous region. Here,a target bounding box surrounds a continuous region of damage. User 250may start from the largest continuous region for determining a targetbounding box, and continue with the next largest continuous region for asubsequent target bounding box in the same image. User 250 can thenselect a part of the continuous region in bounding box 202 with aninternal bounding box 204. In the same way, user 250 can select internalbounding boxes 206 and 208 in bounding box 202.

It should be noted that, even though a bounding box typically takes asquare or rectangular shape, the bounding box may take any other shape,such a triangular or oval shape. In this example, shapes and sizes ofinternal bounding boxes 204, 206, and 208 may take the same or differentforms. Furthermore, two adjacent bounding boxes may or may not be joinedand/or overlapping. In addition, the internal bounding boxes withintarget bounding box 202 may or may not cover the continuous region ofdamage in its entirety.

During operation, system 150 can then determine region proposals forsampling in target bounding box 202. The region proposal can be placedin target bounding box 202 based on a sliding window or an imagesegmentation algorithm. For collecting a positive sample, system 150 maydetermine a region proposal 212 in a region covered by a portion ofdamage 220. System 150 compares region proposal 212 with correspondinginternal bounding boxes 204 and 206 to determine overlapping parameters.Based on whether the overlapping parameters are in a threshold range,system 150 may collect region proposal 212 as a positive sample.

Otherwise, if the overlapping parameters are below a low thresholdrange, system 150 may collect region proposal 212 as a negative sample.In addition, system 150 can also determine a region proposal 214 in aregion of target bounding box 202 that may not include damage 220 (e.g.,using segmentation). System 150 can also determine a region proposal 216outside of target bounding box 202 to collect a negative sample. In thisway, system 150 can use region proposals 214 and 216 for negativesamples, thereby ensuring that the corresponding negative samples do notinclude any damage information.

FIG. 3A illustrates an exemplary region proposal generation process forgenerating image samples, in accordance with an embodiment of thepresent application. System 150 may determine a set of movement rulesthat determines the placement of a region proposal in target boundingbox 202. System 150 can also receive the movement rules as input. Themovement rules can be defined so that, from a current region proposal, asubsequent region proposal can be determined within the region enclosedby target bounding box 202. Such rules can include an initial positionof a region proposal (i.e., the position of a bounding box correspondingto the region proposal), the deviation distance from a previous positionfor a movement, and a movement direction, and a movement terminationcondition. The movement termination condition can be based on one ormore of: a number of region proposals and/or movements in a targetbounding box and the region covered by the region proposals (e.g., athreshold region).

Based on the movement rules, system 150 can determine a number of regionproposals in target bounding box 202. In some embodiments, the upperleft corner of target bounding box 202 is selected as the position forof the initial region proposal 302. The next region proposal 304 can beselected based on a movement from left to right along the left-to-rightwidth of target bounding box 202. A predetermined step length candictate how far region proposal 304 should be from region proposal 302.In this way, a sample can be generated for each movement.

In some further embodiments, the position of a region proposal in targetbounding box 202 can be randomly selected. To do so, system 150 canrandomly determine a reference point of region proposal 302 (e.g., acenter or corner point of region proposal 302). The position of thereference point can be selected based on a movement range of thereference point (e.g., a certain distance between the reference pointand the boundary of target bounding box 202 should be maintained).System 150 can then place region proposal 302 in target bounding box 202based on a predetermined size of a region proposal with respect to thereference point.

FIG. 3B illustrates an exemplary assessment of a region proposal forgenerating image samples, in accordance with an embodiment of thepresent application. Suppose that system 150 has determined a regionproposal 310 in target bounding box 202. To assess region proposal 310,system 150 determines the overlapping parameters that indicate thedegree and/or proportion of overlap between region proposal 310 and theregion enclosed by a respective internal bounding box in target boundingbox 202. Since parts of the continuous region of damage 220 arerepresented by the internal bounding boxes, a high degree of overlapbetween region proposal 310 and the internal bounding boxes indicatesthat region proposal 310 includes a significant portion of damage 220.Based on this assessment, system 150 can select region proposal 310 as apositive sample.

When system 150 performs image sampling in the region enclosed by targetbounding box 202, system 150 may compare region proposal 310 with arespective internal bounding box of target bounding box 202. Thiscomparison can be executed based on an arrangement order of the internalbounding boxes, or based on the distance to region proposal 310 (e.g.,from near to far). In some embodiments, system 150 may compare regionproposal 310 only with the internal bounding boxes in the vicinity ofregion proposal 310. For example, system 150 can compare region proposal310 only with the internal bounding boxes that are within apredetermined threshold distance (e.g., within a 50-pixel distance) or,have an intersection with region proposal 310 (e.g., internal boundingboxes 204, 206, and 208). In this way, system 150 can significantlyreduce the volume of data processing.

System 150 can determine whether region proposal 310 can be an imagesample based on the overlapping parameters of region proposal 310 and asurrounding region. The surrounding region includes the total regionenclosed by region proposal 310 and an internal bounding box that hasbeen compared with region proposal 310. For example, if system 150 hascompared region proposal 310 with internal bounding boxes 204, thesurrounding region for region proposal 310 can be the region enclosed byinternal bounding box 204 and region proposal 310. System 150 can thendetermine the overlapping parameters of region proposal 310 with respectto the internal region, and determine whether region proposal 310 can bean image sample. The overlapping parameters can indicate whether thereis an overlapping, the overlapping degree, and the overlappingproportion.

FIG. 3C illustrates an exemplary determination of whether a regionproposal can be an image sample, in accordance with an embodiment of thepresent application. In this example, system 150 determines whether aregion proposal 354 can be selected as an image sample. System 150 candetermine the surrounding region 356 (denoted with a gray grid) coveredby internal bounding box 352 and region proposal 354. System 150 thendetermines the overlapping region 358 (denoted with a dark line) betweenregion proposal 354 and internal bounding box 352. System 150 can thendetermine the overlapping parameters for internal bounding box 352 andregion proposal 354 as a ratio of overlapping region 358 and surroundingregion 356. In some embodiments, system 150 may determine theoverlapping parameters for internal bounding box 352 and region proposal354 as a ratio of overlapping region 358 and the region enclosed byinternal bounding box 352.

In some embodiments, the ratio is determined based on an intersectionover union (IoU) of the regions. If the regions are represented based onpixels, the ratio can be determined as the ratio of correspondingpixels. In the example in FIG. 3C, if one grid represents one pixel, theratio can be calculated as pixels in overlapping region 358/pixels insurrounding region 356=16/122. If the ratio is larger than apredetermined threshold (e.g., 0.7) or falls within a threshold range(e.g., 0.7-0.99), system 150 can select the region proposal as apositive sample. If system 150 compares a region proposal with aplurality of internal bounding boxes, system 150 can generate a set ofoverlapping parameters. System 150 can select the region proposal as apositive sample if the largest value of the set of overlappingparameters is larger than a threshold or falls within a threshold range.

In some embodiments, the size and shape of the bounding box of a regionproposal may be adjusted to obtain another bounding box. System 150 canthen perform another round of sampling using the new bounding box. Ifthere is another target bounding box in the image, system 150 can usethe same method for image sampling in that target bounding box. In thisway, system 150 can perform image sampling for each target bounding boxin an image. Each target bounding box may allow system 150 to determinea plurality of region proposals for sampling. For each region proposal,system 150 can determine whether to select the region proposal as apositive sample for training a target detector.

Optionally, system 150 does not select a region proposal as a positivesample (i.e., the overlapping parameters have not met the condition),system 150 may further screen the region proposal as a potentialnegative sample. For example, system 150 may select the region proposalas a negative sample if the ratio of the region proposal for eachcorresponding internal bounding box is below a low threshold range(e.g., 0.1). In other words, if the comparison results of a regionproposal and the regions enclosed by all corresponding internal boundingboxes meet the condition for a negative sample, system 150 can selectthe region proposals as a negative sample. Furthermore, system 150 canplace a region proposal outside of any target bounding box to collect anegative sample. Since each continuous damage region is covered by acorresponding target bounding box, a region proposal outside of anytarget bounding box can be selected as a negative sample. In this way,system 150 can reduce noise interference in a negative sample.

Efficient Target Detector

FIG. 4 illustrates an exemplary integration of detection results ofmultiple samples, in accordance with an embodiment of the presentapplication. Suppose that system 150 has obtained positive samples 402,404, and 406 from an input image 400. System 150 can represent samples402, 404, and 406 as inputs 412, 414, and 416, respectively, for targetdetector 160 of system 150. System 150 can use target detector 160 togenerate corresponding outputs 422, 424, and 426. Each of these outputscan include a characteristic description of the corresponding input. Thecharacteristic description can include a feature vector, a label (e.g.,based on the damage class), and a corresponding bounding box.

System 150 can then construct a splice of outputs 422, 424, and 426 inthe bounding box dimension. For example, system 150 can perform aconcatenation 432 of the bounding boxes to improve the accuracy oftarget detector 160. Target detector 160 can be further trained andoptimized based on a Gradient Boosted Decision Trees (GBDT) model 434.GBDT model 434 can optimize the concatenated bounding boxed and generatea corresponding target indicator 436, which can include an optimizedbounding box and a corresponding label, for the damage depicted insamples 402, 404, and 406. In this way, the efficiency and accuracy oftarget detector 160 can be further improved.

Operations

FIG. 5A presents a flowchart 500 illustrating a method of an assessmentsystem performing a damage assessment, in accordance with an embodimentof the present application. During operation, the system receives aninput image indicating damage on a vehicle (operation 502) and performstarget detection on the input image (operation 504). The system thendetermines the damage information (e.g., the degree of the damage andthe parts impacted by the damage) based on the target detection(operation 506). The system assesses the damage based on the damageinformation (e.g., whether the parts can be repaired or would needreplacement) (operation 508). Subsequently, the system determines arepair plan and cost estimate based on the damage assessment andinsurance information (operation 510).

FIG. 5B presents a flowchart 530 illustrating a method of an assessmentsystem generating image samples for training a target detection system,in accordance with an embodiment of the present application. Duringoperation, the system obtains an image for sampling (operation 532) andretrieves a target bounding box and a set of internal bounding boxes inthe target bounding box in the obtained image (operation 534). Thesystem then determines a region proposal in the target bounding boxbased on a set of movement criteria (operation 536) and determinesoverlapping parameters (e.g., IoUs) associated with the region proposaland the corresponding internal bounding boxes (operation 538).

Subsequently, the system determines whether the overlapping parametersare in the threshold range (operation 540). If the overlappingparameters are in the threshold range, the system can select the regionproposal as a positive sample (operation 542). If the overlappingparameters are not in the threshold range, the system determines whetherthe overlapping parameters are below a low threshold range (operation544). If the overlapping parameters are below a low threshold range, thesystem can select the region proposal as a negative sample (operation546).

Upon selecting the region proposal as a sample (operation 542 or 546),or if the overlapping parameters are not below a low threshold range(operation 544), the system determines whether the target bounding boxhas been fully sampled (i.e., the set of movement criteria has met atermination condition) (operation 548). If the target bounding box hasnot been fully sampled, the system determines another region proposal inthe target bounding box based on the set of movement criteria (operation536). If the target bounding box has been fully sampled, the systemdetermines whether the input image has been fully sampled (i.e., alltarget bounding boxes have been sampled) (operation 550). If the inputimage has not been fully sampled, the system retrieves another targetbounding box and another set of internal bounding boxes in the targetbounding box in the obtained image (operation 534).

FIG. 5C presents a flowchart 560 illustrating a method of an assessmentsystem integrating detection results of multiple samples, in accordancewith an embodiment of the present application. During operation, thesystem obtains a training image indicating a damaged area of a vehicle(i.e., a vehicular damage) (operation 562) and a sample associated withthe damage in the image (operation 564). The system then generatescorresponding output comprising features (e.g., a feature vector), abounding box, and a corresponding label using an AI model (e.g., thetarget detector) (operation 566). The system then checks whether allsamples are iterated (operation 570).

If all samples have not been iterated, the system continues to determineanother sample associated with the damage in the image (operation 564).If all samples have been iterated, the system performs bounding boxconcatenation based on the generated outputs to generate acharacteristic description of the damage (operation 572). The systemthen trains and optimizes using a GBDT model (operation 574) and obtainsa bounding box and a corresponding label representing the damage basedon the training and optimization (operation 576).

Exemplary Computer System and Apparatus

FIG. 6 illustrates an exemplary computer system that facilitates anefficient assessment system, in accordance with an embodiment of thepresent application. Computer system 600 includes a processor 602, amemory device 604, and a storage device 608. Memory device 604 caninclude volatile memory (e.g., a dual in-line memory module (DIMM)).Furthermore, computer system 600 can be coupled to a display device 610,a keyboard 612, and a pointing device 614. Storage device 608 can be ahard disk drive (HDD) or a solid-state drive (SSD). Storage device 608can store an operating system 616, a damage assessment system 618, anddata 636. Damage assessment system 618 can facilitate the operations ofsystem 150.

Damage assessment system 618 can include instructions, which whenexecuted by computer system 600 can cause computer system 600 to performmethods and/or processes described in this disclosure. Specifically,damage assessment system 618 can include instructions for generatingregion proposals in a target bounding box of an input image (regionproposal module 620). Damage assessment system 618 can also includeinstructions for calculating overlapping parameters for the regionproposal (parameter module 622). Furthermore, damage assessment system618 includes instructions for determining whether a region proposal canbe a positive or a negative sample based on corresponding thresholds(sampling module 624).

Damage assessment system 618 can also include instructions for trainingand optimizing using a GBDT model (response module 626). Moreover,damage assessment system 618 includes instructions for assessing adamaged area (i.e., a target) of a vehicle from an input image andgenerating a repair plan (planning module 628). Damage assessment system618 may further include instructions for sending and receiving messages(communication module 630). Data 636 can include any data that canfacilitate the operations of damage assessment system 618, such aslabeled images and generated samples.

FIG. 7 illustrates an exemplary apparatus that facilitates an efficientassessment system, in accordance with an embodiment of the presentapplication. Damage assessment apparatus 700 can comprise a plurality ofunits or apparatuses which may communicate with one another via a wired,wireless, quantum light, or electrical communication channel. Apparatus700 may be realized using one or more integrated circuits, and mayinclude fewer or more units or apparatuses than those shown in FIG. 7.Further, apparatus 700 may be integrated in a computer system, orrealized as a separate device that is capable of communicating withother computer systems and/or devices. Specifically, apparatus 700 caninclude units 702-712, which perform functions or operations similar tomodules 620-630 of computer system 600 of FIG. 6, including: a regionproposal unit 702; a parameter unit 704; a sampling unit 706; anoptimization unit 708; a planning unit 710; and a communication unit712.

The data structures and code described in this detailed description aretypically stored on a computer-readable storage medium, which may be anydevice or medium that can store code and/or data for use by a computersystem. The computer-readable storage medium includes, but is notlimited to, volatile memory, non-volatile memory, magnetic and opticalstorage devices such as disks, magnetic tape, CDs (compact discs), DVDs(digital versatile discs or digital video discs), or other media capableof storing computer-readable media now known or later developed.

The methods and processes described in the detailed description sectioncan be embodied as code and/or data, which can be stored in acomputer-readable storage medium as described above. When a computersystem reads and executes the code and/or data stored on thecomputer-readable storage medium, the computer system performs themethods and processes embodied as data structures and code and storedwithin the computer-readable storage medium.

Furthermore, the methods and processes described above can be includedin hardware modules. For example, the hardware modules can include, butare not limited to, application-specific integrated circuit (ASIC)chips, field-programmable gate arrays (FPGAs), and otherprogrammable-logic devices now known or later developed. When thehardware modules are activated, the hardware modules perform the methodsand processes included within the hardware modules.

The foregoing embodiments described herein have been presented forpurposes of illustration and description only. They are not intended tobe exhaustive or to limit the embodiments described herein to the formsdisclosed. Accordingly, many modifications and variations will beapparent to practitioners skilled in the art. Additionally, the abovedisclosure is not intended to limit the embodiments described herein.The scope of the embodiments described herein is defined by the appendedclaims.

What is claimed is:
 1. A method for facilitating image sampling fortraining a target detector, comprising: obtaining a first imagedepicting a first target, wherein a continuous part of the first targetdepicted in the first image is labeled and enclosed by a target boundingbox; determining region proposals from an area of the first imageenclosed by the target bounding box; generating a set of positive imagesamples from a subset of the region proposals, wherein a respectiveregion proposal of the subset of the region proposals is associated withan overlapping parameter in a threshold range, wherein a respectivepositive image sample of the set of positive image samples includes atleast a part of the first target; and training the target detector withthe set of positive image samples to detect a second target from asecond image, wherein the target detector is an artificial intelligence(AI) model capable of detecting an object, and wherein the first andsecond targets indicate first and second vehicular damages,respectively.
 2. The method of claim 1, wherein the label of thecontinuous part indicates a material impacted by the first vehiculardamage.
 3. The method of claim 2, wherein detecting the second targetcomprises detecting the second vehicular damage based on a correspondingmaterial independent of identifying a part of a vehicle impacted by thesecond vehicular damage.
 4. The method of claim 1, wherein theoverlapping parameter is a ratio of an overlapping region of therespective region proposal and a surrounding region of the respectiveregion proposal, wherein the overlapping region indicates a commonregion covered by both the respective region proposal and at least oneinternal bounding box of a set of internal bounding boxes within thetarget bounding box, wherein a respective internal bounding box of theset of internal bounding boxes includes at least a part of thecontinuous region, and wherein the surrounding region indicates a totalregion covered by the respective region proposal and the set of internalbounding boxes.
 5. The method of claim 4, wherein the set of internalbounding boxes is determined for the respective region proposal based onone of: an intersection with the respective region proposal; a distancefrom the respective region proposal; and a total number of internalbounding boxes in the target bounding box.
 6. The method of claim 1,further comprising generating a negative sample, which excludes any partof the first target, from the first image by one or more of: selecting aregion proposal from a second subset of the region proposals as thenegative sample, wherein the overlapping parameter of a respectiveregion proposal of the second subset of the region proposals is in a lowthreshold range; and selecting an area outside of the target boundingbox as the negative sample.
 7. The method of claim 1, wherein the regionproposals are determined based on applying a movement rule.
 8. Themethod of claim 1, further comprising generating a second set ofpositive image samples from one or more of: new region proposals from anarea of the first image enclosed by a second target bounding box in thefirst image; and new region proposals with a different size or shapecompared to the region proposals.
 9. The method of claim 1, furthercomprising optimizing the training of the target detector by: generatinga plurality of bounding boxes for a plurality of image samples in theset of positive image samples, wherein a respective bounding boxidentifies the corresponding part of the continuous region; andcombining the plurality of bounding boxes to generate a combinedbounding box and a corresponding label.
 10. A non-transitorycomputer-readable storage medium storing instructions that when executedby a computer, cause the computer to perform a method for facilitatingimage sampling for training a target detector, the method comprising:obtaining a first image depicting a first target, wherein a continuouspart of the first target depicted in the first image is labeled andenclosed by a target bounding box; determining region proposals from anarea of the first image enclosed by the target bounding box; generatinga set of positive image samples from a subset of the region proposals,wherein a respective region proposal of the subset of the regionproposals is associated with an overlapping parameter in a thresholdrange, wherein a respective positive image sample of the set of positiveimage samples includes at least a part of the first target; and trainingthe target detector with the set of positive image samples to detect asecond target from a second image, wherein the target detector is anartificial intelligence (AI) model capable of detecting an object, andwherein the first and second targets indicate first and second vehiculardamages, respectively.
 11. The non-transitory computer-readable storagemedium of claim 10, wherein the label of the continuous part indicates amaterial impacted by the first vehicular damage.
 12. The non-transitorycomputer-readable storage medium of claim 11, wherein detecting thesecond target comprises detecting the second vehicular damage based on acorresponding material independent of identifying a part of a vehicleimpacted by the second vehicular damage.
 13. The non-transitorycomputer-readable storage medium of claim 10, wherein the overlappingparameter is a ratio of an overlapping region of the respective regionproposal and a surrounding region of the respective region proposal,wherein the overlapping region indicates a common region covered by boththe respective region proposal and at least one internal bounding box ofa set of internal bounding boxes within the target bounding box, whereina respective internal bounding box of the set of internal bounding boxesincludes at least a part of the continuous region, and wherein thesurrounding region indicates a total region covered by the respectiveregion proposal and the set of internal bounding boxes.
 14. Thenon-transitory computer-readable storage medium of claim 13, wherein theset of internal bounding boxes is determined for the respective regionproposal based on one of: an intersection with the respective regionproposal; a distance from the respective region proposal; and a totalnumber of internal bounding boxes in the target bounding box.
 15. Thenon-transitory computer-readable storage medium of claim 10, wherein themethod further comprises generating a negative sample, which excludesany part of the first target, from the first image by one or more of:selecting a region proposal from a second subset of the region proposalsas the negative sample, wherein the overlapping parameter of arespective region proposal of the second subset of the region proposalsis in a low threshold range; and selecting an area outside of the targetbounding box as the negative sample.
 16. The non-transitorycomputer-readable storage medium of claim 10, wherein the regionproposals are determined based on applying a movement rule.
 17. Thenon-transitory computer-readable storage medium of claim 10, wherein themethod further comprises generating a second set of positive imagesamples from one or more of: new region proposals from an area of thefirst image enclosed by a second target bounding box in the first image;and new region proposals with a different size or shape compared to theregion proposals.
 18. The non-transitory computer-readable storagemedium of claim 10, wherein the method further comprises optimizing thetraining of the target detector by: generating a plurality of boundingboxes for a plurality of image samples in the set of positive imagesamples, wherein a respective bounding box identifies the correspondingpart of the continuous region; and combining the plurality of boundingboxes to generate a combined bounding box and a corresponding label.